IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v5y2015i2p2158244015579724.html
   My bibliography  Save this article

Forecasting the 2012 and 2014 Elections Using Bayesian Prediction and Optimization

Author

Listed:
  • Steven E. Rigdon
  • Jason J. Sauppe
  • Sheldon H. Jacobson

Abstract

This article presents a data-driven Bayesian model used to predict the state-by-state winners in the Senate and presidential elections in 2012 and 2014. The Bayesian model takes into account the proportions of polled subjects who favor each candidate and the proportion who are undecided, and produces a posterior probability that each candidate will win each state. From this, a dynamic programming algorithm is used to compute the probability mass functions for the number of electoral votes that each presidential candidate receives and the number of Senate seats that each party receives. On the final day before the 2012 election, the model gave a probability of (essentially) one that President Obama would be reelected, and that the Democrats would retain control of the U.S. Senate. In 2014, the model gave a final probability of .99 that the Republicans would take control of the Senate.

Suggested Citation

  • Steven E. Rigdon & Jason J. Sauppe & Sheldon H. Jacobson, 2015. "Forecasting the 2012 and 2014 Elections Using Bayesian Prediction and Optimization," SAGE Open, , vol. 5(2), pages 21582440155, April.
    • Handle: RePEc:sae:sagope:v:5:y:2015:i:2:p:2158244015579724
    DOI: 10.1177/2158244015579724
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2158244015579724
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2158244015579724?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Drew A. Linzer, 2013. "Dynamic Bayesian Forecasting of Presidential Elections in the States," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 124-134, March.
    2. Brown, Lloyd B. & Chappell Jr., Henry W., 1999. "Forecasting presidential elections using history and polls," International Journal of Forecasting, Elsevier, vol. 15(2), pages 127-135, April.
    3. Christensen, William F. & Florence, Lindsay W., 2008. "Predicting Presidential and Other Multistage Election Outcomes Using State-Level Pre-Election Polls," The American Statistician, American Statistical Association, vol. 62, pages 1-10, February.
    4. Edward H. Kaplan & Arnold Barnett, 2003. "A New Approach to Estimating the Probability of Winning the Presidency," Operations Research, INFORMS, vol. 51(1), pages 32-40, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sebasti'an Morales & Charles Thraves, 2020. "On the Resource Allocation for Political Campaigns," Papers 2012.02856, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kang, Seungwoo & Oh, Hee-Seok, 2024. "Forecasting South Korea’s presidential election via multiparty dynamic Bayesian modeling," International Journal of Forecasting, Elsevier, vol. 40(1), pages 124-141.
    2. Liu, Yezheng & Ye, Chang & Sun, Jianshan & Jiang, Yuanchun & Wang, Hai, 2021. "Modeling undecided voters to forecast elections: From bandwagon behavior and the spiral of silence perspective," International Journal of Forecasting, Elsevier, vol. 37(2), pages 461-483.
    3. repec:cup:judgdm:v:15:y:2020:i:5:p:863-880 is not listed on IDEAS
    4. Ray C. Fair, 2006. "Interpreting the Predictive Uncertainty of Presidential Elections," Levine's Bibliography 321307000000000427, UCLA Department of Economics.
    5. Ray C. Fair, 2006. "Interpreting the Predictive Uncertainty of Elections," Cowles Foundation Discussion Papers 1579, Cowles Foundation for Research in Economics, Yale University, revised May 2007.
    6. Lauderdale, Benjamin E. & Bailey, Delia & Blumenau, Jack & Rivers, Douglas, 2020. "Model-based pre-election polling for national and sub-national outcomes in the US and UK," International Journal of Forecasting, Elsevier, vol. 36(2), pages 399-413.
    7. A. Kamakura, Wagner & Afonso Mazzon, Jose & De Bruyn, Arnaud, 2006. "Modeling voter choice to predict the final outcome of two-stage elections," International Journal of Forecasting, Elsevier, vol. 22(4), pages 689-706.
    8. Ray Fair, 2004. "Predicting Electoral College Victory Probabilities from State Probability Data," Yale School of Management Working Papers amz2406, Yale School of Management.
    9. Richard J. Cebula & Gigi M. Alexander, 2017. "Female Labor Force Participation and Voter Turnout: Evidence from the American Presidential Elections," Review of Economics and Institutions, Università di Perugia, vol. 8(2).
    10. Andrew Gelman & Jessica Hullman & Christopher Wlezien & George Elliott Morris, 2020. "Information, incentives, and goals in election forecasts," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(5), pages 863-880, September.
    11. Jens Großer & Arthur Schram, 2010. "Public Opinion Polls, Voter Turnout, and Welfare: An Experimental Study," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 700-717, July.
    12. Marcelo Tyszler & Arthur Schram, 2016. "Information and strategic voting," Experimental Economics, Springer;Economic Science Association, vol. 19(2), pages 360-381, June.
    13. Ray Fair & Cowles Discussion & Yale Working, 2006. "Interpreting the Predictive Uncertainty of Elections," Yale School of Management Working Papers amz2643, Yale School of Management, revised 01 Aug 2007.
    14. Edieal J. Pinker, 2013. "Introduction to the OR Forum Article: “Blotto Politics”," Operations Research, INFORMS, vol. 61(3), pages 531-531, June.
    15. Ray Fair & Cowles Discussion & Yale Working, 2006. "Interpreting the Predictive Uncertainty of Elections," Yale School of Management Working Papers amz2643, Yale School of Management, revised 01 Aug 2007.
    16. Lauderdale, Benjamin E. & Linzer, Drew, 2015. "Under-performing, over-performing, or just performing? The limitations of fundamentals-based presidential election forecasting," International Journal of Forecasting, Elsevier, vol. 31(3), pages 965-979.
    17. Wang, Samuel S.-H., 2015. "Origins of Presidential poll aggregation: A perspective from 2004 to 2012," International Journal of Forecasting, Elsevier, vol. 31(3), pages 898-909.
    18. Nadeau, Richard & Lewis-Beck, Michael S., 2020. "Election forecasts: Cracking the Danish case," International Journal of Forecasting, Elsevier, vol. 36(3), pages 892-898.
    19. Lesmono, Dharma & Tonkes, Elliot & Burrage, Kevin, 2009. "Opportunistic timing and manipulation in Australian Federal Elections," European Journal of Operational Research, Elsevier, vol. 192(2), pages 677-691, January.
    20. Sen Pei & Jeffrey Shaman, 2020. "Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-19, October.
    21. Ray C. Fair, 2004. "Predicting Electoral College Victory Probabilities from State Probability Data," Cowles Foundation Discussion Papers 1496, Cowles Foundation for Research in Economics, Yale University.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:5:y:2015:i:2:p:2158244015579724. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.